In this research, we ask the question,
if there's a right font for everyone, how can we help them choose?
While we know that font influences reading speed.
The current reading interface does not help us find a faster reading font.
Instead of the commonly seen font selector presents long
and alphabetically sorted list of fonts with very limited guidance.
Typographers we have we have worked with often championed
the importance of personalizing font choices.
Based on interviews with them.
We found that there may not be a one size fits all font.
In fact, different users may read faster in different fonts based on their age,
self-rated reading, speed, and other characteristics as a reader.
Additionally, we have commonly observed in other studies
that readers are often not able to select their fastest fonts simply by preference.
So how can we recommend a faster font to the reader?
We explored the use of machine learning to help accomplish this task.
Today, I will walk you through the findings
from an effort to build a personalized recommender faster fonts.
We start by identifying features that may influence reading speed
because these features can help us estimate reading speed for individuals
based on interviews with typographers and literature reviews.
We identified a suite of font and reader characteristics.
We collect font data through a custom-built web application
and the user data from our readability study.
After experiments with a wide variety of models and structures,
we adopted LambdaMART to help sort fonts based on these input features
to train an ML model like our recommender we need to collect training data.
Our training data comes from the readability study Run on 252 paid
crowd workers.
During the study, they performed reading study that include a font
preference test, reading speed and comprehension tests.
Our objective is not to differentiate the bad from the good fonts,
but rather to select the personalized fastest fonts.
Therefore, we focus our fonts that are considered the prevalent, diverse,
and readable to begin with.
During the Readability Study.
We asked participants to perform the various tests in all eight fonts
shown on the right
in the study.
We again find that the participants were unable
to identify their fastest fonts simply based on their own preference.
In fact, participants preferred fonts with larger X-height
and larger widths such as Merriweather and Poppins.
However, Georgia and Arial are more often their fastest fonts, again
showing the importance of supporting reader with font recommendations.
Now we look at the predictions made by our font recommender FontMART.
we find that it does a much better job than the users to recover the truth
distribution of the fastest fontsWe've evaluated our personalized
font recommendation against various baselines
comparing to them, we find that our personalized recommendation
help participants read faster by 14 to 26 words per minute.
That's equivalent to 2 or more additional letter pages read per hour.
However, on average the ground truth fastest
font is still 46 words per minute faster than our recommended font.
This shows the possibility to improve our recommendation further
with even more training data.
Recall that at the moment we only have 252 participants in our study dataset.
We can also dive deeper to understand why our model works well.
As we expected, a combination of reader and font information helps support
accurate model prediction.
Among all the factors considered, age plays the largest role.
This is a pattern that repeatedly came up during our interview with typographers
as well.
When we look at the font recommendation by quartiles of Age,
we find that Georgia is recommended more often for the older users.
Arial generally works well across age groups, and Poppins
work particularly well for participants between the age of 19 and 25.
Based on the takeaways of the model.
In the absence of fully personalized model,
we can still provide recommendations by age cohort.
Therefore, even though some developers may not be able to fully implement
this personalization model,
our insights can still be implemented easily using a rule based system.